基于并行卷积神经网络的人脸关键点定位方法研究*

为解决人脸关键点定位受到姿态,光线,表情以及遮盖问题的影响而使得定位效果可靠性不佳的问题,提出了一种基于并行卷积神经网络的人脸关键点定位方法。文中利用卷积神经网络对局部细节特征提取以及深度学习特点,设计实现了一种并行卷积神经网络,该网络把人脸图像,上半人脸以及下半人脸分别送入到相同结构的卷积网络进行训练学习,通过对图像进行局部卷积以及下采样,提取人脸关键点附近的细节特征,并对三级并行网络定位结果进行加权合成,实现人脸特征点定位。在LWF人脸库上定位实验结果表明该方法在准确性以及可靠性都得到很大程度提升,能实现对人脸关键点的鲁棒准确估计。     

基于轻量级网络的实时人脸识别算法研究

为了在嵌入式和移动设备上实现高精度的实时人脸识别,对常见的网络在人脸识别方面的优缺点进行了分析,提出了一种高效的深度卷积神经网络模型Lightfacenet。在网络中结合深度可分离卷积、逐点卷积、瓶颈结构和挤压与激励结构提出了轻量化神经网络单元,使网络在保证有一定准确率的情况下有效地解决深层的神经网络带来的参数冗余和计算量大的问题,再通过改进的非线性激活函数进一步提高网络的准确性。该神经网络在保留卷积神经网络部分优点的同时也很好地平衡了网络的缺点。在同样的实验环境下,Lightfacenet网络既实现了非常高的识别精度,也在模型推理速度上达到实时的效果。在使用MS-Celeb-1M数据集训练后,该模型在LFW数据集上达到了99.50%的准确率,其效果已经可以与现在的大型卷积神经网络媲美。对于面部识别,Lightfacenet比目前最先进的移动卷积神经网络在保证准确率的情况下提高了效率。     

一种并行不对称空洞卷积模块

构建卷积神经网络要耗费大量的人力资源,且训练过程中需要消耗大量的算力资源.利用空洞卷积代替卷积神经网络中的池化操作,能有效增加感受野,降低运算复杂度,但是空洞卷积会带来空间层次和信息连续性的丢失.本文提出了一种并行不对称空洞卷积模块,该模块能够补全空洞卷积所丢失的信息,可以嵌入到现有的卷积神经网络中,代替3×3卷积进行网络训练,从而加速网络的收敛,提高网络的性能.实验结果表明,利用本文所提出的并行不对称空洞卷积模块,可以显著提高不同网络在CIFAR-10等数据集上的分类效果.     

基于5G与惯性测量单元的场端定位系统

场端定位指在一定的空间范围内,在场端设施的相互协同下,完成对终端设备的一种特殊定位,可以通过快速移动部署提供精细网格化位置服务。针对终端设备定位精度需求高的实际问题,结合某车企仓储环境下的物流车场端定位需求,开展5G蜂窝网络与惯性测量单元结合的高精度数据融合的研究。通过5G网络所产生的数据与惯性测量单元自带的加速度计与陀螺仪等信息融合进行定位,结合5G信号抗干扰能力强以及惯性测量单元成本较低的优势,使用惯性测量单元的信息将5G数据进行位置修正并在地图上展示。在5G网络覆盖的区域内实现定位且误差在0.5 m以内,波动范围稳定在0.2 m以内。     

卷积神经网络识别地基云图的数据库建立及处理方法

卷积神经网络(Convolutional neural network,CNN)具有非比寻常的从样本中学习特征的能力,训练需要大量带有标签的图像样本。因此,在使用卷积神经网络对地基云图相关研究时,建立云图样本库是第一步,也是非常重要的一步。首先,通过数码相机直接拍摄、从互联网上下载、从公开发行的云图类书籍获取以及由全天空照相机拍摄等手段获取三个云图样本库;接着,对三个样本库图像的分辨率、噪声、数量等问题进行了分析;然后,采用双线性插值和数据增强方法对样本库进行归一化预处理;最后,利用卷积神经网络、LBP、Heinle feature和Texton-based method三种方法对增强后的数据集进行云识别分类验证,实验结果表明,利用本文方法进行增强数据可有效解决卷积神经网络对小样本数据识别率不高以及不能完整进行网络运行的问题,为卷积神经网络在地基云图识别的应用奠定基础。     

基于深度卷积神经网络的图像检索算法研究

为解决卷积神经网络在提取图像特征时所造成的特征信息损失,提高图像检索的准确率,提出了一种基于改进卷积神经网络LeNet-L的图像检索算法。首先,改进LeNet-5卷积神经网络结构,增加网络结构深度。然后,对深度卷积神经网络模型LeNet-L进行预训练,得到训练好的网络模型,进而提取出图像高层语义特征。最后,通过距离函数比较待检图像与图像库的相似度,得出相似图像。在Corel数据集上,与原模型以及传统的SVM主动学习图像检索方法相比,该图像检索方法有较高的准确性。经实验结果表明,改进后的卷积神经网络具有更好的检索效果。     

RF室内定位指纹库空间相关生成算法

基于定位指纹库的模式识别算法是目前基于网络结构（如WLAN）定位技术研究的热点。该技术的关键是根据不同AP（Access Point,AP）的RSSI值建立有效的定位指纹数据库。传统的方法只是直接在定位区域采集样本,不考虑RSSI的空间相关特性,导致离线训练阶段工作量较大。利用RSSI的空间相关特性,提出了通过对传神经网络空间插值建立定位指纹数据库的方法。实验结果表明该方法不但提高了定位精度,而且节省了训练时间,是一种高效的、易于实现的定位指纹数据库生成方法。     

基于多尺度卷积神经网络的交通标志识别

基于多纵卷积神经网络的交通标志识别算法识别率较高,但识别和训练时间较长,实用性较差。为此,构造一种基于多尺度卷积神经网络的道路交通标志识别模型。通过改进单尺度卷积神经网络中特征提取的基网络,将网络不同层级所产生的特征融合为多尺度特征并提供给分类器,以提高低层特征的利用率。在GTSRB数据集上的实验结果表明,该模型准确识别率达到99.25%,与多纵卷积神经网络模型相比,其在保证高精度的同时,识别和训练时间的降幅均超过90%,更适用于真实路况下交通标志的精准检测。     

基于深度时空卷积神经网络的人群异常行为检测和定位

针对公共场合人群异常行为检测准确率不高和训练样本缺乏的问题,提出一种基于深度时空卷积神经网络的人群异常行为检测和定位的方法。首先针对监控视频中人群行为的特点,综合利用静态图像的空间特征和前后帧的时间特征,将二维卷积扩展到三维空间,设计面向人群异常行为检测和定位的深度时空卷积神经网络;为了定位人群异常行为,将视频分成若干子区域,获取视频的子区域时空数据样本,然后将数据样本输入设计的深度时空卷积神经网络进行训练和分类,实现人群异常行为的检测与定位。同时,为了解决深度时空卷积神经网络训练时样本数量不足的问题,设计一种迁移学习的方法,利用样本数量多的数据集预训练网络,然后在待测试的数据集中进行微调和优化网络模型。实验结果表明,该方法在UCSD和subway公开数据集上的检测准确率分别达到了99%和93%以上。     

基于神经网络和路侧单元指纹的车辆定位方法

针对GPS定位盲区无法完成车辆定位,以及现有车辆定位方法精度偏低等问题,提出了一种基于神经网络和路侧单元(RSU)指纹的车辆高精度定位方法.方法将定位区域均匀划分为若干栅格区域,在每个栅格区域中采集不同RSU的接收信号强度(RSSI),构建RSU指纹库.定位阶段,利用反向传播神经网络(BPNN)对待定位车辆的粗略坐标进...     

Only connect: teaching, technology and telesis

Abstract This paper describes an approach to the design of interactive multimedia materials being developed in a European Community project. The developmental process is seen as a dialogue between technologists and teachers. This dialogue is often problematic because of the differences in training, experience and culture between them. Conditions needed for fruitful dialogue are described and the generic model for learning design used in the project is explained.     

European Community policy and the market

Abstract This paper starts with some reflections on the policy considerations and priorities which are shaping European Commission (EC) research programmes. Then it attempts to position the current projects which seek to capitalise on information and communications technologies for learning in relation to these priorities and the apparent realities of the marketplace. It concludes that while there are grounds to be optimistic about the contribution EC programmes can make to the efficiency and standard of education and training, they are still too technology driven.     

一种自适应子融合集成多分类器方法

融合集成方法已经广泛应用在模式识别领域,然而一些基分类器实时性能稳定性较差,导致多分类器融合性能差,针对上述问题本文提出了一种新的基于多分类器的子融合集成分类器系统。该方法考虑在度量层融合层次之上通过对各类基多分类器进行动态选择,票数最多的类别作为融合系统中对特征向量识别的类别,构成一种新的自适应子融合集成分类器方法。实验表明,该方法比传统的分类器以及分类融合方法识别准确率明显更高,具有更好的鲁棒性。     

Avoiding semantic and temporal gaps in developing software intensive systems

Development of software intensive systems (systems) in practice involves a series of self-contained phases for the lifecycle of a system. Semantic and temporal gaps, which occur among phases and among developer disciplines within and across phases, hinder the ongoing development of a system because of the interdependencies among phases and among disciplines. Such gaps are magnified among systems that are developed at different times by different development teams, which may limit reuse of artifacts of systems development and interoperability among the systems. This article discusses such gaps and a systems development process for avoiding them.     

Designing economic np control charts: A programmed simulation approach

This paper presents control charts models and the necessary simulation software for the location of economic values of the control parameters. The simulation program is written in FORTRAN, requires only 10K of main storage, and can run on most mini and micro computers. Two models are presented - one describes the process when it is operating at full capacity and the other when the process is operating under capacity. The models allow the product quality to deteriorate to a further level before an existing out-of-control state is detected, and they can also be used in situations where no prior knowledge exists of the out-of-control causes and the resulting proportion defectives.     

The development of robot art

Going through a few examples of robot artists who are recognized worldwide, we try to analyze the deepest meaning of what is called “robot art” and the related art field definition. We also try to highlight its well-marked borders, such as kinetic sculptures, kinetic art, cyber art, and cyberpunk. A brief excursion into the importance of the context, the message, and its semiotics is also provided, case by case, together with a few hints on the history of this discipline in the light of an artistic perspective. Therefore, the aim of this article is to try to summarize the main characteristics that might classify robot art as a unique and innovative discipline, and to track down some of the principles by which a robotic artifact can or cannot be considered an art piece in terms of social, cultural, and strictly artistic interest. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Explanation and prediction: an architecture for default and abductive reasoning

Although there are many arguments that logic is an appropriate tool for artificial intelligence, there has been a perceived problem with the monotonicity of classical logic. This paper elaborates on the idea that reasoning should be viewed as theory formation where logic tells us the consequences of our assumptions. The two activities of predicting what is expected to be true and explaining observations are considered in a simple theory formation framework. Properties of each activity are discussed, along with a number of proposals as to what should be predicted or accepted as reasonable explanations. An architecture is proposed to combine explanation and prediction into one coherent framework. Algorithms used to implement the system as well as examples from a running implementation are given.     

Three Process Perspectives: Organizations, Teams, and People

This paper provides the author's personal views and perspectives on software process improvement. Starting with his first work on technology assessment in IBM over 20 years ago, Watts Humphrey describes the process improvement work he has been directly involved in. This includes the development of the early process assessment methods, the original design of the CMM, and the introduction of the Personal Software Process (PSP)^SM and Team Software Process (TSP){^SM}. In addition to describing the original motivation for this work, the author also reviews many of the problems he and his associates encountered and why they solved them the way they did. He also comments on the outstanding issues and likely directions for future work. Finally, this work has built on the experiences and contributions of many people. Mr. Humphrey only describes work that he was personally involved in and he names many of the key contributors. However, so many people have been involved in this work that a full list of the important participants would be impractical.     

基于复小波噪声方差显著修正的SAR图像去噪

提出了一种基于复小波域统计建模与噪声方差估计显著性修正相结合的合成孔径雷达(Synthetic Aperture Radar,SAR)图像斑点噪声滤波方法。该方法首先通过对数变换将乘性噪声模型转化为加性噪声模型,然后对变换后的图像进行双树复小波变换(Dualtree Complex Wavelet Transform,DCWT),并对复数小波系数的统计分布进行建模。在此先验分布的基础上,通过运用贝叶斯估计方法从含噪系数中恢复原始系数,达到滤除噪声的目的。实验结果表明该方法在去除噪声的同时保留了图像的细节信息,取得了很好的降噪效果。     

How do children do mathematics with LOGO?

Abstract  This paper considers some results of a study designed to investigate the kinds of mathematical activity undertaken by children (aged between 8 and 11) as they learned to program in LOGO. A model of learning modes is proposed, which attempts to describe the ways in which children used and acquired understanding of the programming/mathematical concepts involved. The remainder of the paper is concerned with discussing the validity and limitations of the model, and its implications for further research and curriculum development.